Food & beverage case study: ₹1.5L to ₹2.4L monthly revenue in 3 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We point the first month at reaching new buyers, the brand’s main problem, on a smaller base. We use cart-value tiers so larger baskets earn a better offer. We set up WhatsApp messages for abandoned checkouts and repeat orders from the start.

   Why

   In this case study, a smaller food and beverage brand grows monthly revenue in 3 months, from ₹1.5L to ₹6L (4x). Fewer than one in ten of our measured accounts grew that fast in the same time, so the case study follows the pace of that top tenth, holds budget where return starts to slip, and does not force the number. The main problem was reaching new buyers, followed by repeat orders. Each extra item in a basket is revenue the ad has already paid for. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   Tier levels are set just above the basket sizes buyers already reach. WhatsApp runs alongside paid retargeting, not instead of it.

2. 2

   Measurement & reviews · Month 1 to 3

   What we do

   We agree a written number for every month on the way from ₹1.5L to ₹6L, and start each review with the month-to-date figure against it. We log store orders every day beside what the ad platform claims. We raise budget in a month only while return holds; where it slips too far, we hold it.

   Why

   It did not report a return on ad spend, so the case study starts from what measured food and beverage stores of that size hold. Written monthly numbers expose a slow month while there is still time to act. The ad platform's own count runs high, so the store's count is the one that moves budget.

   How it works

   The month's number is on the page at every review. The sheet is read before each budget change. A month whose return slips past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and UGC and creator video, building to about two dozen new ads a month by the final month, keeping carousel on a short leash because its return trails the account. We run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget. We buy creative in batches and give each batch a fixed read window before buying more. We run every lead product in a campaign of its own, read every week.

   Why

   An everyday product sells best when the algorithm is free to find its buyers. The read window keeps creative spending tied to evidence. Shared campaigns hide weak products; separate ones expose them fast.

   How it works

   The video that takes off is scaled; the tests around it are cut. Only ads that convert inside the window keep running. Losing products are paused within a week and budget moves to the winners. New ads rise with the budget, most of them video, then catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   We scale through Month 2 to Month 3 as far toward ₹6L as return allows as the budget rises gently while return holds, with Instagram Reels taking the largest share of spend and Facebook Feed the next. We build lookalikes from past buyers once purchases are steady, beside broad prospecting. We make regional-language versions of the winning video for the states that buy most.

   Why

   Across Month 2 to Month 3, the budget rises gently, while return on spend holds. Two of these months stop their budget step where return slips too far. Each budget step here is sized so that return stays close to where it was. Past buyers are the clearest signal of who buys next.

   How it works

   Lookalikes are read against broad on the same creative. Regional cuts run beside the original winner. Most of the final month's spend sits on Instagram Reels, then Facebook Feed. Prospecting to new buyers takes the bulk of spend, while retargeting returns more for each rupee.

1. Month 1

   First, the set-up: a small daily budget carries the first ads, and store orders are matched to tracking. Store orders and ad-platform revenue are checked side by side. WhatsApp checkout recovery is switched on.

   • Revenue ₹1.5L
   • ROAS 1.91x
   • Ad spend ₹79,147
2. Month 2

   Scaling begins: budget shifts to the products that sold this period. The budget step stops where return starts to slip: with budget steps up, return on spend holds.

   • Revenue ₹1.9L
   • ROAS 1.91x
   • Ad spend ₹1L
3. Month 3

   Lookalikes of past buyers are added beside broad prospecting. The budget step stops where return starts to slip: with budget steps up, return on spend holds. Revenue ends below ₹6L, the number this scenario calls for, because budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹2.4L
   • ROAS 1.90x
   • Ad spend ₹1.3L

1. 1

   Cut regional-language versions of the winning creative for the best-selling states.

2. 2

   Buy creative in batches, give each batch a fixed read window, and refresh tired ads by mixing old and new.

3. 3

   Facebook Reels carries the largest share of spend in this industry's measured accounts.